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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국계산역학회 Computers and Concrete, An International Journal Computers and Concrete, An International Journal Vol.21 No.6
- 발행연도
- 2018.1
- 수록면
- 697 - 703 (7page)
이용수
초록· 키워드
오류제보하기
Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.
목차
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참고문헌 (21)
참고문헌 신청
Ahmed M. Ashteyat / 2018 / Predicting residual compressive strength of self-compacted concrete under various temperatures and relative humidity conditions by artificial neural networks / Computers and Concrete, An International Journal 21(1):47~54
Ali Demir / 2015 / Prediction of Hybrid fibre-added concrete strength using artificial neural networks / Computers and Concrete, An International Journal 15(4):503~514
Bhoopal, R.S. / 2012 / Adaptive neuro-fuzzy inference system for prediction of effective thermal conductivity of polymer-matrix composites / Model. Nume. Simul. Mater. Sci. 2:43~50
Bilgehan, M. / 2011 / A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modeling approaches / Nondestruct. Test. Eva. 26(1):35~55
Chiu, S.L. / 1994 / Fuzzy model identification based on cluster estimation / J. Intell. Fuzzy Syst. 2(3):267~278
Ali Demir / 2015 / Prediction of Hybrid fibre-added concrete strength using artificial neural networks / Computers and Concrete, An International Journal 15(4):503~514
Bhoopal, R.S. / 2012 / Adaptive neuro-fuzzy inference system for prediction of effective thermal conductivity of polymer-matrix composites / Model. Nume. Simul. Mater. Sci. 2:43~50
Bilgehan, M. / 2011 / A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modeling approaches / Nondestruct. Test. Eva. 26(1):35~55
Chiu, S.L. / 1994 / Fuzzy model identification based on cluster estimation / J. Intell. Fuzzy Syst. 2(3):267~278
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